{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# notes:\n",
    "* tensorflow is very picky about the shape of data, so even you np.ndarray is `(47, )`, you need to reshape it into `(47,1)` to make sure everything goes alright\n",
    "* the shape of `np.array([1,2,3,4])` is `(4, )`, this is column vector. Be aware of the shape of data.\n",
    "* Different optimizer have very huge difference between convergence speed in this convex example, [I need to learn more about differences of optimizers](http://sebastianruder.com/optimizing-gradient-descent/)\n",
    "* Confused about whether I should use row vector or column vector and here is the answer: [Column Vectors Vs. Row Vectors](http://steve.hollasch.net/cgindex/math/matrix/column-vec.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "\n",
    "from helper import linear_regression as lr  # my own module\n",
    "from helper import general as general\n",
    "\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47, 3)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>square</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.130010</td>\n",
       "      <td>-0.223675</td>\n",
       "      <td>0.475747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.504190</td>\n",
       "      <td>-0.223675</td>\n",
       "      <td>-0.084074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.502476</td>\n",
       "      <td>-0.223675</td>\n",
       "      <td>0.228626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.735723</td>\n",
       "      <td>-1.537767</td>\n",
       "      <td>-0.867025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.257476</td>\n",
       "      <td>1.090417</td>\n",
       "      <td>1.595389</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     square  bedrooms     price\n",
       "0  0.130010 -0.223675  0.475747\n",
       "1 -0.504190 -0.223675 -0.084074\n",
       "2  0.502476 -0.223675  0.228626\n",
       "3 -0.735723 -1.537767 -0.867025\n",
       "4  1.257476  1.090417  1.595389"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv('ex1data2.txt', names=['square', 'bedrooms', 'price'])\n",
    "data = general.normalize_feature(raw_data)\n",
    "\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47, 3) <class 'numpy.ndarray'>\n",
      "(47, 1) <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "X_data = general.get_X(data)\n",
    "print(X_data.shape, type(X_data))\n",
    "\n",
    "y_data = general.get_y(data).reshape(len(X_data), 1)  # special treatment for tensorflow input data\n",
    "print(y_data.shape, type(y_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# run the tensorflow graph over several optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "epoch = 2000\n",
    "alpha = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "optimizer_dict={'GD': tf.train.GradientDescentOptimizer,\n",
    "                'Adagrad': tf.train.AdagradOptimizer,\n",
    "                'Adam': tf.train.AdamOptimizer,\n",
    "                'Ftrl': tf.train.FtrlOptimizer,\n",
    "                'RMS': tf.train.RMSPropOptimizer\n",
    "               }\n",
    "results = []\n",
    "for name in optimizer_dict:\n",
    "    res = lr.linear_regression(X_data, y_data, alpha, epoch, optimizer=optimizer_dict[name])\n",
    "    res['name'] = name\n",
    "    results.append(res)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot them all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f66c022a1d0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABe8AAAMZCAYAAACZBM9iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZIkSZIkST3G8F6SJEmSJEmSpB7zJ1y54OLMVnFlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f66c056d5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(16, 9))\n",
    "\n",
    "for res in results: \n",
    "    loss_data = res['loss']\n",
    "    \n",
    "#     print('for optimizer {}'.format(res['name']))\n",
    "#     print('final parameters\\n', res['parameters'])\n",
    "#     print('final loss={}\\n'.format(loss_data[-1]))\n",
    "    ax.plot(np.arange(len(loss_data)), loss_data, label=res['name'])\n",
    "\n",
    "ax.set_xlabel('epoch', fontsize=18)\n",
    "ax.set_ylabel('cost', fontsize=18)\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "ax.set_title('different optimizer', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
